
While modern deep models have grown more accurate in the past few years, recent researches have shown that their predictions could be over-confident [1]}. Outputting an incorrect but confident decision can have catastrophic consequences. Hence, calibration of uncertainty for DNNs is a critical research topic. Here, the confidence is defined as \(\max _y p(y|\mathbf {x})\)  which is used to decide when to output a prediction. A well-calibrated, but less accurate model can be considerably more useful than a more accurate but less-calibrated model.
